Why feasibility must predict enrollment—not just describe the site—and how to make it inspection-ready

From “profile of a site” to “probability of randomization”

Traditional questionnaires catalog capabilities—beds, scanners, prior trials—but rarely answer the business-critical question: how many randomized participants by when? A predictive feasibility framework flips the script. You ask targeted questions tied to patient flow, pre-screen attrition, scheduling capacity, and local bottlenecks; you score those answers with transparent rules; and you output an enrollment forecast with a confidence range and a contingency plan. This approach builds credibility with study leadership and withstands sponsor and regulator scrutiny because each number is traceable to verifiable artifacts in the TMF/eTMF.

Declare the compliance backbone once—then reuse it everywhere

Ensure your instrument is born audit-ready. Electronic processes align to 21 CFR Part 11 and port neatly to Annex 11; oversight uses ICH E6(R3) terminology; safety signaling references ICH E2B(R3); registry narratives remain consistent with ClinicalTrials.gov in the US and map to EU-CTR postings through CTIS; privacy counts and EHR-based feasibility respect HIPAA and GDPR. All workflows emit a searchable audit trail and route anomalies through CAPA. Anchor your stance with compact in-line links to the FDA, the EMA, the UK’s MHRA, the ICH, the WHO, Japan’s PMDA, and Australia’s TGA so reviewers don’t need a separate references list.

Outcome metrics everyone buys

Define three outcome targets up front: (1) a 13-week enrollment forecast with 80% confidence bounds; (2) a Site Conversion Ratio (pre-screen → consent → randomization) with expected screen failure rate by key inclusion/exclusion; and (3) a startup latency estimate from greenlight to first-patient-in. These become the backbone of your decision meetings, weekly operations, and inspection narrative.

Regulatory mapping—US first, with EU/UK portability baked in

US (FDA) angle—how assessors actually probe feasibility

US reviewers sampling under FDA BIMO look for line-of-sight from a claim (“we can recruit 4/month”) to evidence (EHR cohort counts, referral agreements, past trial conversion, coordinator capacity). They test contemporaneity (when was the data pulled?), attribution (who ran the query?), and retrievability (how quickly can you open the listing and relevant approvals). Your questionnaire and scoring notes should therefore reference data sources explicitly (EHR cube, tumor board logs, screening calendars) and point to where those artifacts live in TMF.

EU/UK (EMA/MHRA) angle—same science, different wrappers

EU/UK review emphasizes transparency, site capacity and capability, and data minimization. If your instrument uses ICH language, locks down personal data, and provides jurisdiction-appropriate wording, it ports with minor wrapper changes. Include quick-switch text for NHS/NIHR contexts (site governance timing, clinic templates) and emphasize public register alignment.

The question domains that truly predict enrollment (and what bad answers look like)

Patient flow & local epidemiology

Ask for counts with filters, not vague “we see many patients.” Example prompts: eligible patients seen last 12 months; new-patient inflow/month; proportion with stable contact details; proportion likely insured for required procedures; competing trials in the same indication; typical time from referral to consent. Red flags: counts reported without time window or filters; “data not available”; copy-pasted figures identical to other sites.

Pre-screen & screening operations

Who runs pre-screens? What tools? What hours? What’s the coordinator:PI ratio on screening days? Ask for scheduling constraints (MRI, infusion chair, endoscopy) and average lead times. Red flags: “PI will screen” (capacity bottleneck); single coordinator across multiple trials; no protected clinic time.

Consent behavior and screen failures

Request historical conversion for similar burden/benefit profiles and ask for top three consent barriers (travel, placebo fears, work conflicts). Ask for mitigation levers the site actually controls (transport vouchers, evening clinics). Red flags: “We do not track” or blanket “80% will consent.”

Startup latency signals

Contracting/IRB turnaround medians, pharmacy mapping lead time, device/software onboarding speed, and past first-patient-in latencies. Red flags: “varies” without numbers; pharmacy “as soon as possible.”

Data and systems readiness

Probe whether site has exportable screening logs, audit-ready calendars, and role-based access to study systems. Ask if their CTMS can exchange site-level forecasts and actuals programmatically. Red flags: manual spreadsheets only; no controlled screening log schema.

1. Require 12-month EHR cohort counts filtered by key criteria (with data steward sign-off).
2. Collect conversion history for similar trials (pre-screen → consent → randomization).
3. Capture coordinator capacity (hours/week) and protected clinic slots for screening.
4. Quantify diagnostic/procedure wait times that gate eligibility timelines.
5. Document startup latencies (contracts, IRB/REC, pharmacy mapping) with medians/IQR.
6. Identify top 3 local consent barriers and site-controlled mitigations.
7. Confirm availability of exportable screening logs with unique IDs.
8. Request formal competing-trial list within 30 miles and site strategy to differentiate.
9. Obtain written referral pathways (internal, network, community partners).
10. Record who owns forecasting (role) and the weekly cadence for updates.

The scoring sheet: weights, confidence, and a defendable math story

Build a weighted model you can explain in two minutes

Keep it simple and transparent. Assign weights to five domains: Patient Flow (30%), Screening Capacity (20%), Startup Latency (15%), Competing Trials (15%), Consent Behavior (20%). Convert site answers to normalized sub-scores (0–100) with clearly published rules. Example: if coordinator hours/week ≥16 and there are two protected screening half-days, Screening Capacity earns ≥85.

From score to forecast with confidence

Translate composite score to an initial monthly forecast using historical analogs, then apply a confidence factor based on data quality (stale EHR pulls, missing logs, unverified referrals). Publish 80% bounds, not a point fantasy. Low data quality widens the interval and downgrades site priority even if the mean looks attractive.

Prevent “gaming” and enforce evidence

For any claim that materially affects the score, require an artifact (EHR cohort screenshot, scheduling report). Add a “credibility” modifier that can subtract up to 10 points for poor evidence. Publish these rules so sites know the bar and the study team can defend down-selection.

Process & evidence: make it rerunnable, traceable, and inspection-proof

Wire data sources into operations

Automate EHR cohort pulls where possible and capture steward attestations with time windows. Store screening logs in a controlled schema with unique IDs and role-based access; route changes through change control. Tie forecasting into CTMS so weekly updates flow without spreadsheets, and enable drill-through from portfolio dashboards to the underlying site listings.

Define oversight hooks (KRIs & actions)

Track KRIs such as consent drop-off, screen failure drivers, and visit lead-time. Use a small set of thresholds with unambiguous actions: if forecast accuracy misses by >30% two cycles in a row, shift budget to better-performing sites or escalate mitigations. Escalation outcomes should feed program risk governance and your QTLs view.

QC / Evidence Pack: what to file where

• RACI, risk register, KRI/QTLs dashboard for feasibility and enrollment.
• System validation (Part 11 / Annex 11), audit trail samples, SOP references.
• Safety interfaces (EHR alerts, adverse event routing) noted per ICH E2B(R3).
• Forecast lineage and traceability (source listings → composite score → portfolio view) using CDISC-aligned terms and example SDTM visit naming where relevant.
• CAPA records for systemic data quality or forecasting issues with effectiveness checks.

The inspection-ready feasibility questionnaire: paste-ready, high-signal items

Patient flow & eligibility filters (quantitative)

Provide 12-month counts of patients meeting inclusion A/B/C and exclusion X/Y/Z; new-patient inflow per month; percent with confirmed contact info; payer mix relevant to required procedures; typical time from diagnosis to specialist appointment; competing trials list and overlap rate.

Screening engine (operational)

Coordinator hours/week; protected clinic half-days for screening; coordinator:PI ratio; diagnostic wait times for eligibility; availability of evening/weekend clinics; access to mobile diagnostics.

Consent behavior (behavioral)

Historical conversion rates by similar burden trials; top 3 consent barriers; mitigations site controls (transport, parking, tele-consent); languages supported; community outreach partnerships.

Startup latency (timeline)

Medians (IQR) for contracts, IRB/REC, pharmacy mapping, system onboarding; last three trials’ first-patient-in latencies; typical bottlenecks and fixes that worked.

Data & systems (traceability)

Screening log system; export capability; role-based access; evidence storage; reconciliation cadence to CTMS; ability to provide weekly forecast deltas with reasons.

Modern realities: decentralized, digital, and human—baked into the score

Decentralized and patient-tech readiness

If your design includes remote activities (DCT) or patient-reported outcomes (eCOA), weight a readiness sub-score: identity assurance, device logistics, broadband coverage, staff training for remote support, and cultural/linguistic suitability of materials. Ask sites how many remote visits/week they can support and what their help-desk coverage looks like.

Equity and community factors

Include indicators that proxy for reaching under-represented populations: local partnerships, clinic hours outside 9–5, availability of interpreters, and transportation solutions. These questions both improve accrual and strengthen your public-facing commitments.

Budget and incentive realism

Ask whether proposed per-patient budgets cover coordinator time, diagnostics, and retention touchpoints. Undercooked budgets lead to quiet disengagement; your scoring sheet should penalize this risk unless the sponsor is willing to adjust.

Turn answers into forecasts—and manage reality every week

The weekly loop

Require sites to submit forecast/actuals deltas with reasons and next-week plan. Consolidate at program level and use simple visuals: funnel (pre-screen→consent→randomization), capacity bar (coordinator hours), and a risk list keyed to KRIs. Keep narrative short; actions matter more than prose.

Re-weight quickly when the field changes

When a competing trial opens or a diagnostic line clears, adjust weights for that domain and publish the new composite quickly. The math is simple; the discipline is keeping a single source of truth and filing the rationale.

Close the loop to quality & safety

Feasibility that ignores safety or data quality is self-defeating. If rapid growth at a site correlates with consent deviations or adverse event under-reporting, throttle back and invest in training. Your governance minutes should show these cause-and-effect checks.

FAQs

How many questions should a predictive feasibility form include?

Keep it to 25–35 high-signal items across five domains (Patient Flow, Screening Capacity, Startup Latency, Competing Trials, Consent Behavior). Each question should either drive the score or populate a decision table—if it does neither, cut it.

How do we validate the scoring sheet?

Back-fit the model to 2–3 completed studies in a similar indication and compare predicted vs actual monthly randomizations. Adjust weights where residuals are persistent. Re-validate after protocol or process changes that affect conversion or capacity.

What evidence must accompany high-impact claims?

Any claim that moves a sub-score >10 points should have a supporting artifact: EHR cohort screenshots with steward signature and date window, scheduling reports, signed referral MOUs, or historical screening logs. File these where your team and inspectors can drill through from the scorecard.

How do we include remote elements fairly?

Create a DCT/ePRO readiness sub-score that tests identity, logistics, staff support, and connectivity. Sites that can support remote visits reliably should score higher because they can convert more interested candidates and maintain retention.

What’s a defensible way to present the forecast?

Provide an 80% confidence interval around the monthly point estimate and clearly state assumptions (referral volume, consent rate, diagnostic capacity). Publish weekly deltas with short reasons and show actions taken when reality diverges from plan.

How do we prevent optimistic responses without proof?

Use a credibility modifier and publish it. If evidence is missing or stale, subtract up to 10 points, widen the confidence interval, and decrease priority in site selection. Re-score promptly when evidence arrives.